Identifying #Addiction Concerns on Twitter during the COVID-19 Pandemic: A Text Mining Analysis

Abstract

Background: The 2019 Novel Coronavirus (COVID-19) is responsible for thousands of deaths and hospitalizations. To curb the spread of this highly transmissible disease, governments enacted protective guidelines for its citizens, including social distancing and stay-at-home orders. These restrictions on social interactions can be especially problematic for individuals managing or recovering from addiction given that treatment often involves access to services and resources that became limited or even unavailable at this time. Social media sites like Twitter serve as a space for users to post questions and concerns about timely topics and allow for researchers to track common themes among the public. The goal of this study was to identify how the public was discussing addiction on Twitter during the COVID pandemic. Methods: We performed a text mining analysis to analyze tweets that contained "addiction" and "covid" to capture posts from the public that illustrated comments and concerns about addiction during the COVID-19 pandemic. We report on 3,301 tweets captured between January 31 and April 23, 2020. The study was conducted in the United States, but contained tweets from multiple countries. Results: The most prevalent topics had to do with services offered by Acadia Healthcare and Serenity Healthcare Centers, attempts to manage time while home, difficulties of coping with alcoholism amidst rising sales of alcohol, and attention to ongoing health crises (e.g.,., opioids, vaping). Additional topics included affordable telehealth services, research from France on the relationship between nicotine and COVID-19, concerns about gambling addiction, and changing patterns in substance misuse as drug availability varies. Conclusions: Analyzing Twitter content enables health professionals to identify the public's concerns about addiction during the COVID-19 pandemic. Findings from text mining studies addressing timely health topics can serve as preliminary analyses for building more comprehensive models, which can then be used to generate recommendations for the larger public and inform policy.

No Apparent Increase in Treatment Uptake for Gambling Disorder during Ten Months of the COVID-19 Pandemic—Analysis of a Regional Specialized Treatment Unit in Sweden

“…However, as the self-reported increase in gambling was seen specifically in problem gamblers in a previous study, an increase in treatment seeking was hypothesized here. Fear of attending physical treatment units has been cited as a barrier to treatment seeking in other addictive disorders [19][20][21] and may represent a factor that decreases the number of patients or masks a potential increase. From the present real-world data, describing a clinical context post hoc, it is clear that distance treatment-on telephone or digital video solutions-represented a large share of contacts taken with the clinic from March 2020 to December 2020, and it is very likely that the treatment uptake would have been lower without the possibility of distance treatment.…”

“…For example, such a possible decrease could be due to home confinement, fears of being exposed to the virus at treatment facilities, and possible changes in provided services. Such changes have been reported to occur in other types of treatment facilities for addictive disorders [19][20][21]. Therefore, there is a need for research studying whether pandemic-related changes in gambling may have translated into an increased-or potentially decreased-uptake of treatment for gambling disorder.…”

Covıd-19 Salgınında Yaşanan Haftalık Duygusal Değişimler: Türkiye Örneği

“…One of the most effective ways to prevent and control outbreaks is to monitor and track news and social networks about the spread of infectious diseases. Glowacki et al (2020) analyze tweets containing #addiction and #covid hashtags using text mining tools to capture conversations about addiction in many countries during the COVİD-19 outbreak. As a result, they have stated that with the increasing (Lu, 2020;Gao et al, 2020) online accessibility during the Covid 19 period, the risk of gambling addiction has become a common health threat (Glowacki, Wilcox and Glowacki, 2020).…”

A novel corpus‐based computing method for handling critical word‐ranking issues: An example of COVID‐19 research articles

“…No doubt, governments around the world are seeking direct and effective measures to mitigate the pandemic and speed up the cure of the confirmed cases 13,14 . With big textual data about COVID‐19 being rapidly distributed, it is critical for humans to rely on machine algorithms to compute important semantic information, thereby, filtering and retrieving critical messages 15,16 . Hence, adopting corpus‐based approaches to process and integrate COVID‐19‐related English‐mediated textual data will enhance frontline medical personnel's efficiency of knowledge acquisition and perception.…”